Despite recent advances in treatment, age-related macular degeneration (AMD) remains the leading cause of irreversible blindness in the elderly population. A shift in the current therapy paradigm will require more sensitive methods of identifying patients at greatest risk for disease development, progression, and poor treatment response. Previously-investigated genetic polymorphisms account for only a portion of AMD risk. Other factors include not only health risks, such as smoking and exposure to other toxins, but also individual metabolism of toxins, drugs, dietary supplements, and perhaps even food. Indeed, comprehensive measurement of metabolites in fluid or tissue has successfully identified risk factors for other chronic diseases, including heart failure, diabetes, and Parkinson's disease. Nevertheless, metabolism is influenced largely by genetic factors. Thus, our long-term goal is to develop profiles combining genetic and metabolic factors to predict disease risk and treatment response in order to improve clinical outcomes for AMD patients. The objective of this proposal is more focused: to discern metabolic profiles related to AMD pathogenesis and determine their relationship to AMD-related genetic variants. Our central hypothesis posits that metabolic profiles combined with genetic variation drive an individual's risk for AMD development, progression, and response to treatment. Using high-resolution liquid chromatography-mass spectrometry (LC-MS) and Sequenom-based genotyping, we will test this hypothesis in two established independent cohorts, along with a new prospective patient cohort recruited from the Vanderbilt Eye Institute. In Aim 1, measuring plasma metabolites in AMD patients and controls will tell us the metabolic differences between these groups and between different stages of AMD. These metabolic variances will point to molecules and pathways that are associated with AMD and could serve as targets for therapeutic intervention. In Aim 2, we will combine these metabolic profiles with genotypes for known AMD-risk genes to determine how metabolites and gene variants interact to influence AMD development and progression. This approach will give us molecular insight into the variability in disease progression among patients. Finally, Aim 3 will prospectively evaluate the impact of metabolic and genetic profiles on intermediate AMD progression and NVAMD treatment response. Successful completion of these aims will provide critical knowledge of metabolic changes associated with AMD and will help identify patients at greatest risk for disease progression and poor treatment response.